{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:Q3ZT63NFYI6RBQUVVTBFIDAYBP","short_pith_number":"pith:Q3ZT63NF","canonical_record":{"source":{"id":"2507.09753","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-13T19:16:11Z","cross_cats_sorted":["q-bio.QM"],"title_canon_sha256":"2e894bbc20805debf46f8041ce3fa63baa5023c27df7d72ee3b5076ca4fabc81","abstract_canon_sha256":"9e3f21dd5fbaf78a27469e7a8df8c9ff6a0794a3a2454b2c9ac24071c95d5a0f"},"schema_version":"1.0"},"canonical_sha256":"86f33f6da5c23d10c295acc2540c180bc1fbc0d2f64cb43f1cc5b1d7661d0552","source":{"kind":"arxiv","id":"2507.09753","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.09753","created_at":"2026-07-05T11:36:39Z"},{"alias_kind":"arxiv_version","alias_value":"2507.09753v1","created_at":"2026-07-05T11:36:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.09753","created_at":"2026-07-05T11:36:39Z"},{"alias_kind":"pith_short_12","alias_value":"Q3ZT63NFYI6R","created_at":"2026-07-05T11:36:39Z"},{"alias_kind":"pith_short_16","alias_value":"Q3ZT63NFYI6RBQUV","created_at":"2026-07-05T11:36:39Z"},{"alias_kind":"pith_short_8","alias_value":"Q3ZT63NF","created_at":"2026-07-05T11:36:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:Q3ZT63NFYI6RBQUVVTBFIDAYBP","target":"record","payload":{"canonical_record":{"source":{"id":"2507.09753","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-13T19:16:11Z","cross_cats_sorted":["q-bio.QM"],"title_canon_sha256":"2e894bbc20805debf46f8041ce3fa63baa5023c27df7d72ee3b5076ca4fabc81","abstract_canon_sha256":"9e3f21dd5fbaf78a27469e7a8df8c9ff6a0794a3a2454b2c9ac24071c95d5a0f"},"schema_version":"1.0"},"canonical_sha256":"86f33f6da5c23d10c295acc2540c180bc1fbc0d2f64cb43f1cc5b1d7661d0552","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:36:39.207116Z","signature_b64":"gQu5ahgO1dEB6mpUOJwNuoGtbOBtENJMCKTK4lWCxX9Jyl+P3Fc9DxjWd2+kIat/mjbQAw624V5H0Q1Zn/RUCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"86f33f6da5c23d10c295acc2540c180bc1fbc0d2f64cb43f1cc5b1d7661d0552","last_reissued_at":"2026-07-05T11:36:39.206525Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:36:39.206525Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.09753","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:36:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EABBaK+AC5aAH8bnqegYtkmz2FjXS0OC5uqcYjwD/RPmcPXF8gTEJBC8lf+1vIBaEOTh54QRROCNa/H2W5WYCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T01:24:11.166046Z"},"content_sha256":"d26b40f89e336295c933c8d3df3b551125178fb0bf86c3864a13046ddb9a686f","schema_version":"1.0","event_id":"sha256:d26b40f89e336295c933c8d3df3b551125178fb0bf86c3864a13046ddb9a686f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:Q3ZT63NFYI6RBQUVVTBFIDAYBP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Do we need equivariant models for molecule generation?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.QM"],"primary_cat":"cs.LG","authors_text":"Andrew Martin Watkins, Ewa M. Nowara, Joshua Rackers, Max Shen, Michael Maser, Pan Kessel, Patricia Suriana","submitted_at":"2025-07-13T19:16:11Z","abstract_excerpt":"Deep generative models are increasingly used for molecular discovery, with most recent approaches relying on equivariant graph neural networks (GNNs) under the assumption that explicit equivariance is essential for generating high-quality 3D molecules. However, these models are complex, difficult to train, and scale poorly.\n  We investigate whether non-equivariant convolutional neural networks (CNNs) trained with rotation augmentations can learn equivariance and match the performance of equivariant models. We derive a loss decomposition that separates prediction error from equivariance error, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.09753","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2507.09753/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:36:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RjVI4Js423DwUT+5GKHFdxt/8CL7LhR2DD9/0Rwqa5+J9DgHTcZVdzoYjLinx7/16V56IjEopwUi7fx48cKDBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T01:24:11.166917Z"},"content_sha256":"063706764ed9280848b2d23a94eaddab18c6d349d7dcbd3c28df1b7b7f10f5b3","schema_version":"1.0","event_id":"sha256:063706764ed9280848b2d23a94eaddab18c6d349d7dcbd3c28df1b7b7f10f5b3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Q3ZT63NFYI6RBQUVVTBFIDAYBP/bundle.json","state_url":"https://pith.science/pith/Q3ZT63NFYI6RBQUVVTBFIDAYBP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Q3ZT63NFYI6RBQUVVTBFIDAYBP/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-07T01:24:11Z","links":{"resolver":"https://pith.science/pith/Q3ZT63NFYI6RBQUVVTBFIDAYBP","bundle":"https://pith.science/pith/Q3ZT63NFYI6RBQUVVTBFIDAYBP/bundle.json","state":"https://pith.science/pith/Q3ZT63NFYI6RBQUVVTBFIDAYBP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Q3ZT63NFYI6RBQUVVTBFIDAYBP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:Q3ZT63NFYI6RBQUVVTBFIDAYBP","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9e3f21dd5fbaf78a27469e7a8df8c9ff6a0794a3a2454b2c9ac24071c95d5a0f","cross_cats_sorted":["q-bio.QM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-13T19:16:11Z","title_canon_sha256":"2e894bbc20805debf46f8041ce3fa63baa5023c27df7d72ee3b5076ca4fabc81"},"schema_version":"1.0","source":{"id":"2507.09753","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.09753","created_at":"2026-07-05T11:36:39Z"},{"alias_kind":"arxiv_version","alias_value":"2507.09753v1","created_at":"2026-07-05T11:36:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.09753","created_at":"2026-07-05T11:36:39Z"},{"alias_kind":"pith_short_12","alias_value":"Q3ZT63NFYI6R","created_at":"2026-07-05T11:36:39Z"},{"alias_kind":"pith_short_16","alias_value":"Q3ZT63NFYI6RBQUV","created_at":"2026-07-05T11:36:39Z"},{"alias_kind":"pith_short_8","alias_value":"Q3ZT63NF","created_at":"2026-07-05T11:36:39Z"}],"graph_snapshots":[{"event_id":"sha256:063706764ed9280848b2d23a94eaddab18c6d349d7dcbd3c28df1b7b7f10f5b3","target":"graph","created_at":"2026-07-05T11:36:39Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2507.09753/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep generative models are increasingly used for molecular discovery, with most recent approaches relying on equivariant graph neural networks (GNNs) under the assumption that explicit equivariance is essential for generating high-quality 3D molecules. However, these models are complex, difficult to train, and scale poorly.\n  We investigate whether non-equivariant convolutional neural networks (CNNs) trained with rotation augmentations can learn equivariance and match the performance of equivariant models. We derive a loss decomposition that separates prediction error from equivariance error, ","authors_text":"Andrew Martin Watkins, Ewa M. Nowara, Joshua Rackers, Max Shen, Michael Maser, Pan Kessel, Patricia Suriana","cross_cats":["q-bio.QM"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-13T19:16:11Z","title":"Do we need equivariant models for molecule generation?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.09753","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d26b40f89e336295c933c8d3df3b551125178fb0bf86c3864a13046ddb9a686f","target":"record","created_at":"2026-07-05T11:36:39Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9e3f21dd5fbaf78a27469e7a8df8c9ff6a0794a3a2454b2c9ac24071c95d5a0f","cross_cats_sorted":["q-bio.QM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-07-13T19:16:11Z","title_canon_sha256":"2e894bbc20805debf46f8041ce3fa63baa5023c27df7d72ee3b5076ca4fabc81"},"schema_version":"1.0","source":{"id":"2507.09753","kind":"arxiv","version":1}},"canonical_sha256":"86f33f6da5c23d10c295acc2540c180bc1fbc0d2f64cb43f1cc5b1d7661d0552","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"86f33f6da5c23d10c295acc2540c180bc1fbc0d2f64cb43f1cc5b1d7661d0552","first_computed_at":"2026-07-05T11:36:39.206525Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:36:39.206525Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gQu5ahgO1dEB6mpUOJwNuoGtbOBtENJMCKTK4lWCxX9Jyl+P3Fc9DxjWd2+kIat/mjbQAw624V5H0Q1Zn/RUCA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:36:39.207116Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.09753","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d26b40f89e336295c933c8d3df3b551125178fb0bf86c3864a13046ddb9a686f","sha256:063706764ed9280848b2d23a94eaddab18c6d349d7dcbd3c28df1b7b7f10f5b3"],"state_sha256":"76c0d9a79a1eb51c4fb6330f9604bd816e17b9fe7533f503de90d70ddd388bbf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ycUze8EuQoJMARCHCPeC1xh5jRCFyzKQx+5xUkDVjGsOOZQtnKcUnpQs23c3lrjF0gtQyqw0OLn8fCJWv3EfBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T01:24:11.174014Z","bundle_sha256":"bd26accc33f1143711d72f31177fab3a364573384ceb7df9bdc493454b8cc8ff"}}